Resolving Contrasts Error in R Linear Models: 4 Essential Solutions

Based on the provided code, it appears that the main issue is with the lm function in R, which throws an error when trying to fit a linear model due to “contrasts can be applied only to factors with 2 or more levels”.

To resolve this error, several solutions can be explored:

Drop the offending variable: If there’s no statistical reason to keep the variable with new levels, dropping it from the model is a simple solution.

# Create a sample dataset
set.seed(123)
df <- data.frame(x = rnorm(100), y = rnorm(100))
df$x <- factor(df$x)

# Fit the linear model without the offending variable
model <- lm(y ~ x + other_variable, data = df)

# Print the coefficients
summary(model)

Replace with a vector of 1: If there’s no need to drop the variable but want to resolve the issue, replace it in the model formula with a vector of 1.

# Create a sample dataset
set.seed(123)
df <- data.frame(x = rnorm(100), y = rnorm(100))
df$x <- factor(df$x)

# Fit the linear model with the offending variable replaced by a vector of 1
model <- lm(y ~ 1 + other_variable, data = df)

# Print the coefficients
summary(model)

Adjust the model formula per group: If fitting separate models for each category is feasible, this approach allows you to dynamically generate model formulae.

# Create a sample dataset with categorical variables
set.seed(123)
df <- data.frame(x = rnorm(100), y = rnorm(100))
df$group <- factor(rep(c("A", "B"), 50))

# Define the model function for each group
model_group_A <- function() {
  lm(y ~ x + other_variable, data = df[df$group == "A", ])
}

model_group_B <- function() {
  lm(y ~ x + other_variable, data = df[df$group == "B", ])
}

# Fit the models for each group
model_A <- model_group_A()
model_B <- model_group_B()

# Print the coefficients
summary(model_A)
summary(model_B)

Use cross-validation: If no statistical solution is possible, using cross-validation can help you estimate the performance of your model on unseen data.

# Create a sample dataset with categorical variables
set.seed(123)
df <- data.frame(x = rnorm(100), y = rnorm(100))
df$group <- factor(rep(c("A", "B"), 50))

# Define the model function for each group
model_group_A <- function() {
  lm(y ~ x + other_variable, data = df[df$group == "A", ])
}

model_group_B <- function() {
  lm(y ~ x + other_variable, data = df[df$group == "B", ])
}

# Define the cross-validation function
cv_function <- function(model) {
  # Split the data into training and testing sets
  set.seed(123)
  idx_train <- sample(1:nrow(df), nrow(df) * 0.8, replace = TRUE)
  df_train <- df[idx_train, ]
  df_test <- df[-idx_train, ]

  # Fit the model on the training data
  model_train <- model()
  summary(model_train)

  # Predict on the testing data
  predictions <- predict(model_train, newdata = df_test)

  # Calculate the mean squared error
  mse <- mean((df_test$y - predictions)^2)
  return(mse)
}

# Define the cross-validation grid
param_grid <- expand.grid(method = c("model_A", "model_B"))

# Perform the cross-validation
results <- lapply(param_grid, function(param) {
  model <- ifelse(param$method == "model_A", model_group_A(), model_group_B())
  cv_function(model)
})

# Print the results
summary(results)

These solutions can help you resolve the “contrasts error” in R when working with linear models.

Last modified on 2025-05-03